Overview

Dataset statistics

Number of variables23
Number of observations3678
Missing cells6712
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory668.1 B

Variable types

Categorical13
Numeric10

Alerts

society has a high cardinality: 676 distinct valuesHigh cardinality
sector has a high cardinality: 115 distinct valuesHigh cardinality
areaWithType has a high cardinality: 2355 distinct valuesHigh cardinality
price is highly overall correlated with price_per_sqft and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
area is highly overall correlated with price and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with price and 5 other fieldsHigh correlation
bathroom is highly overall correlated with price and 5 other fieldsHigh correlation
super_built_up_area is highly overall correlated with price and 7 other fieldsHigh correlation
built_up_area is highly overall correlated with price and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with price and 5 other fieldsHigh correlation
property_type is highly overall correlated with price and 2 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
store room is highly imbalanced (55.7%)Imbalance
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1803 (49.0%) missing valuesMissing
built_up_area has 1988 (54.1%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73500562)Skewed
built_up_area is highly skewed (γ1 = 40.70657243)Skewed
carpet_area is highly skewed (γ1 = 24.33967469)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 462 (12.6%) zerosZeros

Reproduction

Analysis started2024-09-13 17:24:22.262227
Analysis finished2024-09-13 17:24:55.330393
Duration33.07 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.7 KiB
flat
2819 
house
859 

Length

Max length5
Median length4
Mean length4.2335508
Min length4

Characters and Unicode

Total characters15571
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhouse
2nd rowflat
3rd rowhouse
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Length

2024-09-13T17:24:55.478382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:24:55.745967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15571
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15571
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

society
Categorical

Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size294.0 KiB
independent
486 
tulip violet
 
75
ss the leaf
 
73
shapoorji pallonji joyville gurugram
 
42
dlf new town heights
 
42
Other values (671)
2959 

Length

Max length49
Median length39
Mean length16.870003
Min length1

Characters and Unicode

Total characters62031
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)8.3%

Sample

1st rowarjun marg/ sector- 26 phase- 1/ golf course road
2nd rowpivotal riddhi siddhi
3rd rowunitech espace
4th rowgodrej nature plus
5th rowgodrej nature plus

Common Values

ValueCountFrequency (%)
independent 486
 
13.2%
tulip violet 75
 
2.0%
ss the leaf 73
 
2.0%
shapoorji pallonji joyville gurugram 42
 
1.1%
dlf new town heights 42
 
1.1%
signature global park 35
 
1.0%
shree vardhman victoria 34
 
0.9%
smart world orchard 32
 
0.9%
emaar mgf emerald floors premier 32
 
0.9%
dlf the ultima 31
 
0.8%
Other values (666) 2795
76.0%

Length

2024-09-13T17:24:55.992258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.5%
heights 134
 
1.4%
Other values (783) 7500
77.5%

Most occurring characters

ValueCountFrequency (%)
e 6710
 
10.8%
6005
 
9.7%
a 5861
 
9.4%
r 4173
 
6.7%
n 4163
 
6.7%
i 3831
 
6.2%
t 3719
 
6.0%
s 3473
 
5.6%
l 2946
 
4.7%
o 2758
 
4.4%
Other values (31) 18392
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55481
89.4%
Space Separator 6005
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4173
 
7.5%
n 4163
 
7.5%
i 3831
 
6.9%
t 3719
 
6.7%
s 3473
 
6.3%
l 2946
 
5.3%
o 2758
 
5.0%
d 2489
 
4.5%
Other values (16) 15358
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
9 13
 
2.5%
0 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6005
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55481
89.4%
Common 6550
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4173
 
7.5%
n 4163
 
7.5%
i 3831
 
6.9%
t 3719
 
6.7%
s 3473
 
6.3%
l 2946
 
5.3%
o 2758
 
5.0%
d 2489
 
4.5%
Other values (16) 15358
27.7%
Common
ValueCountFrequency (%)
6005
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
9 13
 
0.2%
0 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6710
 
10.8%
6005
 
9.7%
a 5861
 
9.4%
r 4173
 
6.7%
n 4163
 
6.7%
i 3831
 
6.2%
t 3719
 
6.0%
s 3473
 
5.6%
l 2946
 
4.7%
o 2758
 
4.4%
Other values (31) 18392
29.6%

sector
Categorical

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
sohna road
 
153
sector 85
 
108
sector 102
 
107
sector 92
 
99
sector 69
 
93
Other values (110)
3118 

Length

Max length26
Median length9
Mean length9.3175639
Min length3

Characters and Unicode

Total characters34270
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 26
2nd rowsector 99
3rd rowsector 50
4th rowsector 33
5th rowsector 33

Common Values

ValueCountFrequency (%)
sohna road 153
 
4.2%
sector 85 108
 
2.9%
sector 102 107
 
2.9%
sector 92 99
 
2.7%
sector 69 93
 
2.5%
sector 90 89
 
2.4%
sector 81 87
 
2.4%
sector 65 87
 
2.4%
sector 109 85
 
2.3%
sector 79 76
 
2.1%
Other values (105) 2694
73.2%

Length

2024-09-13T17:24:56.252951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector 3450
46.7%
road 177
 
2.4%
sohna 165
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 99
 
1.3%
69 93
 
1.3%
90 89
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (107) 2923
39.6%

Most occurring characters

ValueCountFrequency (%)
o 3803
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3549
10.4%
c 3501
10.2%
t 3461
10.1%
1 1074
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6207
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23305
68.0%
Decimal Number 7258
 
21.2%
Space Separator 3707
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3803
16.3%
s 3694
15.9%
r 3694
15.9%
e 3549
15.2%
c 3501
15.0%
t 3461
14.9%
a 697
 
3.0%
d 248
 
1.1%
n 229
 
1.0%
h 202
 
0.9%
Other values (10) 227
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1074
14.8%
0 802
11.0%
8 778
10.7%
9 762
10.5%
6 740
10.2%
7 682
9.4%
2 679
9.4%
3 666
9.2%
5 592
8.2%
4 483
6.7%
Space Separator
ValueCountFrequency (%)
3707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23305
68.0%
Common 10965
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3803
16.3%
s 3694
15.9%
r 3694
15.9%
e 3549
15.2%
c 3501
15.0%
t 3461
14.9%
a 697
 
3.0%
d 248
 
1.1%
n 229
 
1.0%
h 202
 
0.9%
Other values (10) 227
 
1.0%
Common
ValueCountFrequency (%)
3707
33.8%
1 1074
 
9.8%
0 802
 
7.3%
8 778
 
7.1%
9 762
 
6.9%
6 740
 
6.7%
7 682
 
6.2%
2 679
 
6.2%
3 666
 
6.1%
5 592
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3803
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3549
10.4%
c 3501
10.2%
t 3461
10.1%
1 1074
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6207
18.1%

price
Real number (ℝ)

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5341464
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:24:56.494232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9803593
Coefficient of variation (CV)1.1760801
Kurtosis14.932733
Mean2.5341464
Median Absolute Deviation (MAD)0.72
Skewness3.2787171
Sum9277.51
Variance8.8825413
MonotonicityNot monotonic
2024-09-13T17:24:56.769539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.5 64
 
1.7%
1.2 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.5%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3059
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13892.662
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:24:57.201994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716
Q16818
median9020
Q313878
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7060

Descriptive statistics

Standard deviation23206.896
Coefficient of variation (CV)1.6704427
Kurtosis186.97985
Mean13892.662
Median Absolute Deviation (MAD)2795
Skewness11.438752
Sum50861036
Variance5.3856003 × 108
MonotonicityNot monotonic
2024-09-13T17:24:57.654665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
22222 13
 
0.4%
6666 13
 
0.4%
11111 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3510
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.389
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:24:58.125616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11233
median1733
Q32300
95-th percentile4246
Maximum875000
Range874950
Interquartile range (IQR)1067

Descriptive statistics

Standard deviation23164.341
Coefficient of variation (CV)8.0198136
Kurtosis942.28654
Mean2888.389
Median Absolute Deviation (MAD)533
Skewness29.735006
Sum10574392
Variance5.365867 × 108
MonotonicityNot monotonic
2024-09-13T17:24:58.585387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
3240 43
 
1.2%
1950 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3268
88.9%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%

areaWithType
Categorical

Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.2 KiB
Plot area 360(301.01 sq.m.)
 
37
Plot area 300(250.84 sq.m.)
 
26
Plot area 502(419.74 sq.m.)
 
19
Plot area 200(167.23 sq.m.)
 
19
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)
 
17
Other values (2350)
3560 

Length

Max length124
Median length119
Mean length54.229201
Min length12

Characters and Unicode

Total characters199455
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowPlot area 1000(836.13 sq.m.)
2nd rowCarpet area: 706
3rd rowPlot area 360(301.01 sq.m.)
4th rowBuilt Up area: 1996 (185.43 sq.m.)
5th rowCarpet area: 76.44

Common Values

ValueCountFrequency (%)
Plot area 360(301.01 sq.m.) 37
 
1.0%
Plot area 300(250.84 sq.m.) 26
 
0.7%
Plot area 502(419.74 sq.m.) 19
 
0.5%
Plot area 200(167.23 sq.m.) 19
 
0.5%
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.) 17
 
0.5%
Super Built up area 1578(146.6 sq.m.) 17
 
0.5%
Plot area 270(225.75 sq.m.) 17
 
0.5%
Super Built up area 1350(125.42 sq.m.) 15
 
0.4%
Super Built up area 2010(186.74 sq.m.) 14
 
0.4%
Super Built up area 1650(153.29 sq.m.)Carpet area: 1022.58 sq.ft. (95 sq.m.) 14
 
0.4%
Other values (2345) 3483
94.7%

Length

2024-09-13T17:24:59.061358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area 5574
18.5%
sq.m 3656
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 684
 
2.3%
plot 681
 
2.3%
Other values (2846) 8702
28.9%

Most occurring characters

ValueCountFrequency (%)
26468
 
13.3%
. 20391
 
10.2%
a 13157
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9206
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6770
 
3.4%
Other values (25) 82358
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82770
41.5%
Decimal Number 47142
23.6%
Space Separator 26468
 
13.3%
Other Punctuation 23409
 
11.7%
Uppercase Letter 8594
 
4.3%
Close Punctuation 5536
 
2.8%
Open Punctuation 5536
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13157
15.9%
r 9458
11.4%
e 9322
11.3%
s 7568
9.1%
q 7432
9.0%
t 7325
8.8%
u 6770
8.2%
p 6768
8.2%
m 5545
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
ValueCountFrequency (%)
1 9206
19.5%
0 6630
14.1%
2 5689
12.1%
5 4714
10.0%
3 3961
8.4%
4 3711
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3159
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1873
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20391
87.1%
: 3018
 
12.9%
Space Separator
ValueCountFrequency (%)
26468
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5536
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108091
54.2%
Latin 91364
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13157
14.4%
r 9458
10.4%
e 9322
10.2%
s 7568
8.3%
q 7432
8.1%
t 7325
8.0%
u 6770
7.4%
p 6768
7.4%
m 5545
 
6.1%
l 3701
 
4.1%
Other values (10) 14318
15.7%
Common
ValueCountFrequency (%)
26468
24.5%
. 20391
18.9%
1 9206
 
8.5%
0 6630
 
6.1%
2 5689
 
5.3%
) 5536
 
5.1%
( 5536
 
5.1%
5 4714
 
4.4%
3 3961
 
3.7%
4 3711
 
3.4%
Other values (5) 16249
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26468
 
13.3%
. 20391
 
10.2%
a 13157
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9206
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6770
 
3.4%
Other values (25) 82358
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3602501
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:24:59.503495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8974002
Coefficient of variation (CV)0.5646604
Kurtosis18.216047
Mean3.3602501
Median Absolute Deviation (MAD)1
Skewness3.4851888
Sum12359
Variance3.6001274
MonotonicityNot monotonic
2024-09-13T17:24:59.913194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 942
25.6%
4 661
18.0%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1496
40.7%
4 661
18.0%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4249592
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:25:00.328896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9479764
Coefficient of variation (CV)0.56875901
Kurtosis17.537825
Mean3.4249592
Median Absolute Deviation (MAD)1
Skewness3.2478883
Sum12597
Variance3.7946121
MonotonicityNot monotonic
2024-09-13T17:25:00.543947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1047
28.5%
4 820
22.3%
5 295
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 820
22.3%
5 295
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
3+
1173 
3
1074 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3189233
Min length1

Characters and Unicode

Total characters4851
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row2
3rd row3+
4th row0
5th row0

Common Values

ValueCountFrequency (%)
3+ 1173
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
4.9%

Length

2024-09-13T17:25:00.780712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:01.044457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 2247
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2247
46.3%
+ 1173
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
75.8%
Math Symbol 1173
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2247
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4851
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2247
46.3%
+ 1173
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2247
46.3%
+ 1173
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7974857
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:25:01.280848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0118103
Coefficient of variation (CV)0.88441678
Kurtosis4.5174311
Mean6.7974857
Median Absolute Deviation (MAD)3
Skewness1.6941339
Sum24872
Variance36.141864
MonotonicityNot monotonic
2024-09-13T17:25:01.551255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 317
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 317
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size225.5 KiB
East
624 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.837068
Min length4

Characters and Unicode

Total characters18002
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth-East
2nd rowEast
3rd rowEast
4th rowNorth
5th rowSouth

Common Values

ValueCountFrequency (%)
East 624
17.0%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2024-09-13T17:25:01.799546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:02.094972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
east 624
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3775
21.0%
s 2015
11.2%
o 1760
9.8%
h 1760
9.8%
E 1420
 
7.9%
a 1420
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13085
72.7%
Uppercase Letter 3775
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3775
28.8%
s 2015
15.4%
o 1760
13.5%
h 1760
13.5%
a 1420
 
10.9%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1420
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16860
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3775
22.4%
s 2015
12.0%
o 1760
10.4%
h 1760
10.4%
E 1420
 
8.4%
a 1420
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3775
21.0%
s 2015
11.2%
o 1760
9.8%
h 1760
9.8%
E 1420
 
7.9%
a 1420
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.5 KiB
Relatively New
1646 
New Property
594 
Moderately Old
563 
Undefined
306 
Old Property
303 

Length

Max length18
Median length14
Mean length13.385536
Min length9

Characters and Unicode

Total characters49232
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Old
2nd rowRelatively New
3rd rowModerately Old
4th rowUndefined
5th rowUnder Construction

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 594
 
16.2%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 266
 
7.2%

Length

2024-09-13T17:25:02.334971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:02.585963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
new 2240
31.8%
relatively 1646
23.3%
property 897
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2307
 
4.7%
N 2240
 
4.5%
w 2240
 
4.5%
i 2218
 
4.5%
Other values (15) 14068
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38810
78.8%
Uppercase Letter 7050
 
14.3%
Space Separator 3372
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8433
21.7%
l 4721
12.2%
t 3638
9.4%
y 3106
 
8.0%
r 2889
 
7.4%
d 2307
 
5.9%
w 2240
 
5.8%
i 2218
 
5.7%
a 2209
 
5.7%
o 1992
 
5.1%
Other values (7) 5057
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2240
31.8%
R 1646
23.3%
P 897
12.7%
O 866
 
12.3%
U 572
 
8.1%
M 563
 
8.0%
C 266
 
3.8%
Space Separator
ValueCountFrequency (%)
3372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45860
93.2%
Common 3372
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8433
18.4%
l 4721
 
10.3%
t 3638
 
7.9%
y 3106
 
6.8%
r 2889
 
6.3%
d 2307
 
5.0%
N 2240
 
4.9%
w 2240
 
4.9%
i 2218
 
4.8%
a 2209
 
4.8%
Other values (14) 11859
25.9%
Common
ValueCountFrequency (%)
3372
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2307
 
4.7%
N 2240
 
4.5%
w 2240
 
4.5%
i 2218
 
4.5%
Other values (15) 14068
28.6%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.6%
Missing1803
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:25:02.866878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.6
Variance583959.12
MonotonicityNot monotonic
2024-09-13T17:25:03.180262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1803
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct644
Distinct (%)38.1%
Missing1988
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:25:03.456232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2024-09-13T17:25:03.734959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1988
54.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct733
Distinct (%)39.1%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.4843
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:25:04.019091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1845
median1300
Q31790
95-th percentile2970
Maximum607936
Range607921
Interquartile range (IQR)945

Descriptive statistics

Standard deviation22793.75
Coefficient of variation (CV)9.0112242
Kurtosis604.8596
Mean2529.4843
Median Absolute Deviation (MAD)475
Skewness24.339675
Sum4737724
Variance5.1955503 × 108
MonotonicityNot monotonic
2024-09-13T17:25:04.806065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (723) 1579
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2972 
1
706 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Length

2024-09-13T17:25:05.111343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:05.352602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2349 
1
1329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2349
63.9%
1 1329
36.1%

Length

2024-09-13T17:25:05.554482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:05.797251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2349
63.9%
1 1329
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2349
63.9%
1 1329
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2349
63.9%
1 1329
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2349
63.9%
1 1329
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2349
63.9%
1 1329
36.1%

store room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3340 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Length

2024-09-13T17:25:06.001420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:06.276701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3021 
1
657 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3021
82.1%
1 657
 
17.9%

Length

2024-09-13T17:25:06.475465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:06.704000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3021
82.1%
1 657
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 3021
82.1%
1 657
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3021
82.1%
1 657
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3021
82.1%
1 657
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3021
82.1%
1 657
 
17.9%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3273 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Length

2024-09-13T17:25:06.902717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:07.260686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2417 
2
1055 
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2417
65.7%
2 1055
28.7%
1 206
 
5.6%

Length

2024-09-13T17:25:07.748402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T17:25:08.012251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2417
65.7%
2 1055
28.7%
1 206
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2417
65.7%
2 1055
28.7%
1 206
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2417
65.7%
2 1055
28.7%
1 206
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2417
65.7%
2 1055
28.7%
1 206
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2417
65.7%
2 1055
28.7%
1 206
 
5.6%

luxury_score
Real number (ℝ)

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.517401
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-09-13T17:25:08.352157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.052563
Coefficient of variation (CV)0.74181336
Kurtosis-0.87989139
Mean71.517401
Median Absolute Deviation (MAD)38
Skewness0.45884513
Sum263041
Variance2814.5745
MonotonicityNot monotonic
2024-09-13T17:25:08.826399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2314
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2024-09-13T17:24:50.857367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:24.827678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:27.279660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:30.422237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:34.458912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:37.018687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:39.559346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:42.230146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:45.470196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:48.420310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:51.104242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:25.063760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:27.565353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:31.863342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:34.718910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:37.266571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:39.783871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:42.462918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:45.831504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:48.652049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:51.349167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:25.306793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:27.799667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:32.227610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:34.985333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:37.528096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:40.036780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:42.705436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:46.218852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:48.902275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:51.608392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:25.540978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:28.025508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:32.546715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:35.223157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:37.755606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:40.248496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:42.938129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:46.618528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:49.141796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:51.860247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:25.799707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:28.279212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:32.920430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:35.498683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:38.041716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:40.774641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:43.246420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:46.942984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:49.392869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:52.119346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:26.047122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:28.565674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:33.249565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:35.750921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:38.291610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:41.042864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:43.620817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:47.209840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:49.663107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:52.699928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:26.274163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:28.797326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:33.484085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:36.001806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:38.547525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:41.264170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:43.986283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:47.464557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:49.894144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:52.936670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:26.541065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:29.036232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:33.733842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:36.244294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:38.768000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:41.496563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:44.383658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:47.673728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:50.123660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:53.197101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:26.787659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:29.452840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:33.978529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:36.510237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:39.036192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:41.740868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:44.727803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:47.923823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:50.340797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:53.441424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:27.031303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:29.799181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:34.219169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:36.758268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:39.285686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:41.980164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:45.064488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:48.143920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-09-13T17:24:50.599253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-09-13T17:25:09.211342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
priceprice_per_sqftareabedRoombathroomfloorNumsuper_built_up_areabuilt_up_areacarpet_arealuxury_scoreproperty_typebalconyfacingagePossessionstudy roomservant roomstore roompooja roomothersfurnishing_type
price1.0000.7440.7440.6810.7200.0010.7720.6050.6140.2150.5420.1360.0210.1020.2440.3690.3030.3350.0340.175
price_per_sqft0.7441.0000.2070.4170.411-0.1260.2870.1320.1370.0540.2010.0330.0000.0560.0300.0440.0000.0430.0360.022
area0.7440.2071.0000.6240.6870.1160.9480.8350.8010.2590.0280.0110.0220.0000.0180.0150.0390.0370.0420.043
bedRoom0.6810.4170.6241.0000.862-0.1040.8000.3800.5690.0570.5950.1760.0320.1290.1550.3170.2230.2910.0800.167
bathroom0.7200.4110.6870.8621.000-0.0050.8190.4650.5990.1790.4710.2260.0440.1110.1760.5200.2440.2860.0700.198
floorNum0.001-0.1260.116-0.104-0.0051.0000.1520.0910.1580.2320.4840.0790.0000.1250.0790.0830.1120.1030.0330.017
super_built_up_area0.7720.2870.9480.8000.8190.1521.0000.9260.8940.2221.0000.3060.0000.0860.1210.5840.0460.1570.0840.134
built_up_area0.6050.1320.8350.3800.4650.0910.9261.0000.9690.2890.0000.0001.0000.0000.0000.0000.0000.0000.0000.088
carpet_area0.6140.1370.8010.5690.5990.1580.8940.9691.0000.2390.0000.0260.0000.0000.0000.0000.0000.0000.0160.000
luxury_score0.2150.0540.2590.0570.1790.2320.2220.2890.2391.0000.3290.2230.0650.2550.1830.3470.2280.1890.1760.244
property_type0.5420.2010.0280.5950.4710.4841.0000.0000.0000.3291.0000.2140.0940.3790.1270.0650.2410.2510.0260.080
balcony0.1360.0330.0110.1760.2260.0790.3060.0000.0260.2230.2141.0000.0160.2740.1830.4410.1460.1970.0810.178
facing0.0210.0000.0220.0320.0440.0000.0001.0000.0000.0650.0940.0161.0000.0920.0000.0350.0350.0270.0000.049
agePossession0.1020.0560.0000.1290.1110.1250.0860.0000.0000.2550.3790.2740.0921.0000.1410.2860.1430.1860.1080.214
study room0.2440.0300.0180.1550.1760.0790.1210.0000.0000.1830.1270.1830.0000.1411.0000.1850.2260.3140.0310.141
servant room0.3690.0440.0150.3170.5200.0830.5840.0000.0000.3470.0650.4410.0350.2860.1851.0000.1610.2520.0000.270
store room0.3030.0000.0390.2230.2440.1120.0460.0000.0000.2280.2410.1460.0350.1430.2260.1611.0000.3050.1060.157
pooja room0.3350.0430.0370.2910.2860.1030.1570.0000.0000.1890.2510.1970.0270.1860.3140.2520.3051.0000.0330.216
others0.0340.0360.0420.0800.0700.0330.0840.0000.0160.1760.0260.0810.0000.1080.0310.0000.1060.0331.0000.060
furnishing_type0.1750.0220.0430.1670.1980.0170.1340.0880.0000.2440.0800.1780.0490.2140.1410.2700.1570.2160.0601.000

Missing values

2024-09-13T17:24:53.893075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-13T17:24:54.588058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-13T17:24:55.059458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0housearjun marg/ sector- 26 phase- 1/ golf course roadsector 2631.5035000.09000.0Plot area 1000(836.13 sq.m.)793+3.0North-EastModerately OldNaN9000.0NaN11110174
1flatpivotal riddhi siddhisector 990.72947.07603.0Carpet area: 70622212.0NaNRelatively NewNaNNaN706.0000100031
2houseunitech espacesector 5010.3031790.03240.0Plot area 360(301.01 sq.m.)563+3.0EastModerately OldNaN3240.0NaN111102160
3flatgodrej nature plussector 331.758768.01996.0Built Up area: 1996 (185.43 sq.m.)3302.0NaNUndefinedNaN1996.0NaN00000056
4flatgodrej nature plussector 331.2013369.0898.0Carpet area: 76.4422011.0NaNUnder ConstructionNaNNaN76.4400000056
5houseindependentsector 4315.5028233.05490.0Plot area 610(510.04 sq.m.)5633.0EastModerately OldNaN5490.0NaN11110076
6flatbreez global hill viewsohna road0.355319.0658.0Built Up area: 658 (61.13 sq.m.)Carpet area: 554.17 sq.ft. (51.48 sq.m.)22218.0NaNNew PropertyNaN658.0554.1700000015
7flatemaar gurgaon greenssector 1021.408484.01650.0Super Built up area 1650(153.29 sq.m.)3334.0NorthRelatively New1650.0NaNNaN01000283
8flatvatika gurgaonsector 831.155808.01980.0Super Built up area 1980(183.95 sq.m.)Built Up area: 1350 sq.ft. (125.42 sq.m.)Carpet area: 1308 sq.ft. (121.52 sq.m.)3323.0SouthRelatively New1980.01350.01308.00111102165
9housess aaron villesector 496.5018808.03456.0Plot area 384(321.07 sq.m.)5522.0North-EastRelatively NewNaN3456.0NaN11010228
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793houseindependentsector 2614.7551864.02844.0Plot area 316(264.22 sq.m.)16203+4.0EastNew PropertyNaN2844.0NaN111102153
3794housevipul tatvam villasector 488.5026235.03240.0Plot area 360(301.01 sq.m.)441NaNNaNRelatively NewNaN3240.0NaN00000021
3795housevipul tatvam villasector 486.4024691.02592.0Plot area 288(240.8 sq.m.)Built Up area: 240 sq.yards (200.67 sq.m.)Carpet area: 200 sq.yards (167.23 sq.m.)3432.0NorthRelatively NewNaN240.0200.000000111002148
3796flatrailway officers rpf societysector 9a1.256921.01806.0Carpet area: 1806 (167.78 sq.m.)4331.0NaNOld PropertyNaNNaN1806.00000001000040
3797flatemaar palm gardenssector 831.809473.01900.0Super Built up area 1900(176.52 sq.m.)333+9.0SouthRelatively New1900.0NaNNaN00000055
3798flatrof anandasector 950.325827.0549.0Carpet area: 549.16 (51.02 sq.m.)2219.0NorthRelatively NewNaNNaN549.17417800010271
3799flatraheja vedaantasector 1080.955214.01822.0Super Built up area 1822(169.27 sq.m.)3333.0NaNRelatively New1822.0NaNNaN00001095
3800flatbreez global heightssohna road0.215329.0394.0Carpet area: 394 (36.6 sq.m.)1112.0NaNRelatively NewNaNNaN394.00000000000021
3801houseindependentsector 5011.5835741.03240.0Plot area 360(301.01 sq.m.)5532.0NaNModerately OldNaN3240.0NaN01000020
3802flatpioneer arayasector 628.3519513.04279.0Super Built up area 4279(397.53 sq.m.)46316.0EastRelatively New4279.0NaNNaN010100153